Statistical inference for structured high-dimensional models. Abstracts from the workshop held March 11--17, 2018
type: Item
instance of: scholarly article
title: Statistical inference for structured high-dimensional models. Abstracts from the workshop held March 11--17, 2018
zbMATH Open document ID: 1409.00096
DOI: 10.4171/OWR/2018/12
published in: Oberwolfach Reports
publication date: 2019-03-15T00:00:00Z
review text: Summary: High-dimensional statistical inference is a newly emerged direction of statistical science in the 21 century. Its importance is due to the increasing dimensionality and complexity of models needed to process and understand the modern real world data. The main idea making possible meaningful inference about such models is to assume suitable lower dimensional underlying structure or low-dimensional approximations, for which the error can be reasonably controlled. Several types of such structures have been recently introduced including sparse high-dimensional regression, sparse and/or low rank matrix models, matrix completion models, dictionary learning, network models (stochastic block model, mixed membership models) and more. The workshop focused on recent developments in structured sequence and regression models, matrix and tensor estimation, robustness, statistical learning in complex settings, network data, and topic models.
Mathematics Subject Classification ID: 00B05
Mathematics Subject Classification ID: 00B25
Mathematics Subject Classification ID: 62Gxx
Mathematics Subject Classification ID: 62-06
zbMATH DE Number: 7036470
describes a project that uses: Z_LINEAR_K
describes a project that uses: ROptEst
describes a project that uses: Resultants
MaRDI profile type: Publication
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full work available at URL: https://doi.org/10.4171/owr/2018/12
OpenAlex ID: W2908176697
Wikidata QID: Q128638805
description: scientific article; zbMATH DE number 7036470